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英国
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Pattern Recognition 中科院1区 JCR:Q1 SCIE EI PubMed JST
发文量 14,106
被引量 793,629
影响因子(2025版) 7.860

Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Over the intervening years it has expanded considerably. The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. Papers whos primary concern falls outside the pattern recognition domain and which report routine applications of it using existing or well known methods, should be directed elsewhere. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition. Submitted papers should be single column, double spaced, no less than 20 and no more than 35 (40 for a review) pages long, with numbered pages. Benefits to authors We also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more.

  • 主办单位： ELSEVIER SCI LTD
  • 出版地区： LONDON
  • 出版周期： 月刊
  • 别名： PATTERN RECOGN;Pattern Recognit.;PATTERN RECOGNITION;模式识别
  • 国际标准连续出版物号/电子版： ISSN 0031-3203 / EISSN 1873-5142
  • 创刊时间： 1968年
热门主题：

PATTERN RECOGNITIONFace RecognitionFeature ExtractionSupervised LearningComputer VisionImage SegmentationFeature SelectionArt PerformanceObject RecognitionDeep LearningDimensionality ReductionConvolutional Neural NetworksObject DetectionSupport Vector MachinesNeural NetworksConvolutional Neural NetworkImage ProcessingFeature SpaceImage RetrievalSupport Vector Machine

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